Computational Discovery of Aggregation-Mediated Dipeptide Inhibitors Targeting PHLDA1 for Cardiovascular Therapy

Area of Science:

• Computational biology and structural bioinformatics.
• Cardiovascular research and drug discovery.

Background:

• Cardiovascular diseases (CVDs) are a major global health concern, necessitating innovative treatments.
• The pleckstrin homology-like domain family A member 1-encoded protein (PEP) is implicated in cardiomyocyte apoptosis and presents a potential therapeutic target.

Purpose of the Study:

• To computationally characterize PEP's structure and dynamics.
• To identify high-affinity peptide inhibitors for PEP.
• To explore novel therapeutic strategies for cardiovascular diseases.

Main Methods:

• Integrated computational approach combining AlphaFold3 structure prediction and molecular dynamics (MD) simulations.
• High-throughput screening of dipeptides for PEP binding affinity.
• Analysis of force field performance and protein dynamics.

Main Results:

• PEP exhibits significant intrinsic disorder, with MD simulations refining its conformation.
• CHARMM-class force fields demonstrated superior performance in maintaining PEP's secondary structures.
• The dipeptide FF was identified as the strongest binder, utilizing an aggregation-mediated mechanism with multivalent interactions.
• Low-confidence regions in AlphaFold3 predictions were found to be functionally relevant in binding.

Conclusions:

• PEP is a tractable therapeutic target for cardiovascular diseases.
• The dipeptide FF represents a promising lead for developing aggregation-prone peptide therapeutics.
• This study provides a novel framework for designing peptide-based drugs targeting intrinsically disordered proteins.